Image generation and manipulation algorithms with deep learning. Convolutional neural networks, generative adversarial networks. (multi-modal) image to image translation. Image inpainting. Texture synthesis. Unsupervised feature learning via image reconstruction. 3D image learning. Video generation.
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Course Learning Outcomes: Course Learning Outcome Assessment
Introduction to Deep Generative Networks Convolutional Neural Network Architectures Generative Adversarial Networks Discovering Interpretable Latent Codes Transformers, Attention Models Image to Image Translation Multi-modal Image to Image Translation Presentations Image Inpainting Texture Synthesis Unsupervised Feature Learning via Image Reconstruction Self-supervised 3D Image Learning Video Generation Project Presentations ECTS - Workload Table: Activities Number Hours Workload Project (including preparation and presentation if applicable) 1 30 30 Individual or group work 14 2 28 Homework 1 10 10 Preparation for Midterm exam 1 10 10 Midterm exam 1 2 2 Preparation for Final exam 1 12 12 Report (including preparation and presentation if applicable) 1 10 10 Final exam 1 2 2 Course hours 14 3 42 Total Workload: 146 Total Workload / 30: 146 / 30 4.87 ECTS Credits of the Course: 5 Type of Course: Independent Study - Lecture - Project - Research Study Course Material: Lecture Notes - Multimedia - LMS (Moodle, etc) Teaching Methods: Assignment - Independent study - Discussion - Presentations - Lecturing